Industry increasingly depends upon highly automated data acquisition and control systems to ensure that industrial processes are run efficiently, safely and reliably while lowering their overall production costs. Data acquisition begins when a number of sensors measure aspects of an industrial process and periodically report their measurements back to a data collection and control system. Such measurements come in a wide variety of forms. By way of example the measurements produced by a sensor/recorder include: a temperature, a pressure, a pH, a mass/volume flow of material, a tallied inventory of packages waiting in a shipping line, or a photograph of a room in a factory. Often sophisticated process management and control software examines the incoming data, produces status reports, and, in many cases, responds by sending commands to actuators/controllers that adjust the operation of at least a portion of the industrial process. The data produced by the sensors also allow an operator to perform a number of supervisory tasks including: tailor the process (e.g., specify new set points) in response to varying external conditions (including costs of raw materials), detect an inefficient/non-optimal operating condition and/or impending equipment failure, and take remedial actions such as move equipment into and out of service as required.
Typical industrial processes are extremely complex and receive substantially greater volumes of information than any human could possibly digest in its raw form. By way of example, it is not unheard of to have thousands of sensors and control elements (e.g., valve actuators) monitoring/controlling aspects of a multi-stage process within an industrial plant. These sensors are of varied type and report on varied characteristics of the process. Their outputs are similarly varied in the meaning of their measurements, in the amount of data sent for each measurement, and in the frequency of their measurements. As regards the latter, for accuracy and to enable quick response, some of these sensors/control elements take one or more measurements every second. Multiplying a single sensor/control element by thousands of sensors/control elements (a typical industrial control environment) results in an overwhelming volume of data flowing into the manufacturing information and process control system. Sophisticated data management and process visualization techniques have been developed to handle the large volumes of data generated by such system.
Highly advanced human-machine interface/process visualization systems exist today that are linked to data sources such as the above-described sensors and controllers. Such systems acquire and digest (e.g., filter) the process data described above. The digested process data in-turn drives a graphical display rendered by a human machine interface. An example of such system is the well-known Wonderware INTOUCH® human-machine interface (HMI) software system for visualizing and controlling a wide variety of industrial processes. An INTOUCH HMI process visualization application includes a set of graphical views of a particular process. Each view, in turn, comprises one or more graphical elements. The graphical elements are “animated” in the sense that their display state changes over time in response to associated/linked data sources. For example, a view of a refining process potentially includes a tank graphical element. The tank graphical element has a visual indicator showing the level of a liquid contained within the tank, and the level indicator of the graphical element rises and falls in response to a steam of data supplied by a tank level sensor indicative of the liquid level within the tank. Animated graphical images driven by constantly changing process data values within data streams, of which the tank level indicator is only one example, are considerably easier for a human observer to comprehend than a steam of numbers. For this reason process visualization systems, such as INTOUCH, have become essential components of supervisory process control and manufacturing information systems.
An exemplary environment within which a unified equipment model for supervisory control and data acquisition (SCADA) and manufacturing execution system (MES) is implemented is described, for example in Krajewski, III, et al. U.S. application Ser. No. 10/943,301 which corresponds to US App. Pub. 2006/0056285 A1, the contents of which are incorporated herein by reference in their entirety, including any references contained therein. The MES is, by way of example, the FACTELLIGENCE MES product of Invensys, Systems, Inc. which is another primary component of an illustrative environment within which unified model is employed. The MES differs from the SCADA component in that it is not generally used to exert supervisory control over a plant/manufacturing process. Instead, the MES monitors production and records various production/manufacturing events and applies known business rules to render decisions governing production operations carried out by the SCADA system. MES systems interface to higher level enterprise resource planning (ERP) systems.